Homogenization and analysis of an expanded long-term monthly rainfall network for the Island of Ireland (1850–2010)

Long-term precipitation series are critical for understanding emerging changes to the hydrological cycle. To this end we construct a homogenized Island of Ireland Precipitation (IIP) network comprising 25 stations and a composite series covering the period 1850–2010, providing the second-longest regional precipitation archive in the British-Irish Isles. We expand the existing catalogue of long-term precipitation records for the island by recovering archived data for an additional eight stations. Following bridging and updating of stations HOMogenisation softwarE in R (HOMER) homogenization software is used to detect breaks using pairwise and joint detection. A total of 25 breakpoints are detected across 14 stations, and the majority (20) are corroborated by metadata. Assessment of variability and change in homogenized and extended precipitation records reveal positive (winter) and negative (summer) trends. Trends in records covering the typical period of digitization (1941 onwards) are not always representative of longer records. Furthermore, trends in post-homogenization series change magnitude and even direction at some stations. While cautionary flags are raised for some series, confidence in the derived network is high given attention paid to metadata, coherence of behaviour across the network and consistency of findings with other long-term climatic series such as England and Wales precipitation. As far as we are aware, this work represents the first application of HOMER to a long-term precipitation network and bodes well for use in other regions. It is expected that the homogenized IIP network will find wider utility in benchmarking and supporting climate services across the Island of Ireland, a sentinel location in the North Atlantic.

Overweight and obesity on the island of Ireland: an estimation of costs

Objectives The increasing prevalence of overweight and obesity worldwide continues to compromise population health and creates a wider societal cost in terms of productivity loss and premature mortality. Despite extensive international literature on the cost of overweight and obesity, findings are inconsistent between Europe and the USA, and particularly within Europe. Studies vary on issues of focus, specific costs and methods. This study aims to estimate the healthcare and productivity costs of overweight and obesity for the island of Ireland in 2009, using both top-down and bottom-up approaches.

Methods Costs were estimated across four categories: healthcare utilisation, drug costs, work absenteeism and premature mortality. Healthcare costs were estimated using Population Attributable Fractions (PAFs). PAFs were applied to national cost data for hospital care and drug prescribing. PAFs were also applied to social welfare and national mortality data to estimate productivity costs due to absenteeism and premature mortality.

Results The healthcare costs of overweight and obesity in 2009 were estimated at €437 million for the Republic of Ireland (ROI) and €127.41 million for NI. Productivity loss due to overweight and obesity was up to €865 million for ROI and €362 million for NI. The main drivers of healthcare costs are cardiovascular disease, type II diabetes, colon cancer, stroke and gallbladder disease. In terms of absenteeism, low back pain is the main driver in both jurisdictions, and for productivity loss due to premature mortality the primary driver of cost is coronary heart disease.

Conclusions The costs are substantial, and urgent public health action is required in Ireland to address the problem of increasing prevalence of overweight and obesity, which if left unchecked will lead to unsustainable cost escalation within the health service and unacceptable societal costs.

A genomic island linked to ecotype divergence in Atlantic cod

The genomic architecture underlying ecological divergence and ecological speciation with gene flow is still largely unknown for most organisms. One central question is whether divergence is genome-wide or localized in 'genomic mosaics' during early stages when gene flow is still pronounced. Empirical work has so far been limited, and the relative impacts of gene flow and natural selection on genomic patterns have not been fully explored. Here, we use ecotypes of Atlantic cod to investigate genomic patterns of diversity and population differentiation in a natural system characterized by high gene flow and large effective population sizes, properties which theoretically could restrict divergence in local genomic regions. We identify a genomic region of strong population differentiation, extending over approximately 20 cM, between pairs of migratory and stationary ecotypes examined at two different localities. Furthermore, the region is characterized by markedly reduced levels of genetic diversity in migratory ecotype samples. The results highlight the genomic region, or 'genomic island', as potentially associated with ecological divergence and suggest the involvement of a selective sweep. Finally, we also confirm earlier findings of localized genomic differentiation in three other linkage groups associated with divergence among eastern Atlantic populations. Thus, although the underlying mechanisms are still unknown, the results suggest that 'genomic mosaics' of differentiation may even be found under high levels of gene flow and that marine fishes may provide insightful model systems for studying and identifying initial targets of selection during ecological divergence.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

The role of societal norms in portion size related behaviour in Denmark and on the Island of Ireland

Purpose:
Social norms influence eating behavior, but little is known about their role in portion size-related behavior. This study
explored the role of social eating norms in Denmark (DK) and the Island of Ireland (IOI) in relation to portion size-related
behavior.

Methods:
In a survey DK (n=1063) and IOI (n=1012) respondents rated social eating norms (11 items) and portion size-related behavior
(3 items) on a 7-point scale (1=strongly disagree to 7=strongly agree). The 3 items relate to: 1) anticipating how
much will be eaten at the beginning of a meal, 2) clearing the plate, and 3) clearing the plate even when full. Sociodemographics
and eating attitudes (e.g. cognitive restraint) were measured as background variables

Results:
Two social eating factors were identified: The ‘limit intake’ norm (6 items) and the ‘plate cleaning’ norm (3 items). The
DK participants reported stronger ‘limit intake’ norms and weaker ‘plate cleaning’ norms than IOI. In both countries
females reported stronger ‘limit intake’ norms while males reported stronger ‘plate cleaning’ norms. In DK, age was
positively correlated with both social eating norm factors. The ‘limit intake’ norm had stronger association with anticipating
how much will be eaten at the beginning of a meal, but the ‘plate cleaning’ norm had stronger association with
clearing the plate. Only the ‘plate cleaning’ norm was associated with clearing the plate even when full.

Conclusions:
The social eating norms vary significantly between countries and genders. The ‘limit intake’ and ‘plate cleaning’ norms
play a role in consumers’ reported portion size-related behavior.

Potential impacts of climate change on agriculture and food safety within the island of Ireland

Climate and other environmental change presents a number of challenges for effective food safety. Food production, distribution and consumption takes place within functioning ecosystems but this backdrop is often ignored or treated as static and unchanging. The risks presented by environmental change include novel pests and diseases, often caused by problem species expanding their spatial distributions as they track changing conditions, toxin generation in crops, direct effects on crop and animal production, consequences for trade networks driven by shifting economic viability of production methods in changing environments and finally, wholesale transformation of ecosystems as they respond to novel climatic regimes.

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.